class_docstr: A recommender based on the similarity between item content rather
  using user interaction patterns to compute similarity.
  
  **Creating an ItemContentRecommender**
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.recommender.item_content_recommender.create`
  to create an instance
  of this model. A detailed list of parameter options and code samples
  are available in the documentation for the create function.
  
  Notes
  -----
  **Model Definition**
  
  This model first computes the similarity between items using the
  content of each item. The similarity score between two items is
  calculated by first computing the similarity between the item data
  for each column, then taking a weighted average of the per-column
  similarities to get the final similarity.  The recommendations are
  generated according to the average similarity of a candidate item
  to all the items in a user's set of rated items.
  
  For more examples, see the associated `create` function.
code: def _native_name(cls):
      return "item_content_recommender"
  
file_path: turicreate/src/python/turicreate/toolkits/recommender/item_content_recommender.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate import SFrame
  from turicreate.toolkits.recommender.util import _Recommender
label_desc: Use image node_id 2 for calling the ItemContentRecommender obj's underlying member method code with example usage: obj._native_name(cls) and returns: str